Ocena poziomu aktywności fizycznej osób zatrudnionych w sektorze IT podczas pandemii COVID-19

Introduction. Assessing the physical activity of IT workers during the COVID-19 pandemic can help to discern its hypothetical relationship with gender, the form of work, or other factors.Material and methods. The study lasted from July 29, 2021 to September 14, 2021. For the assessment, a questionnaire was conducted based on the IPAQ-SF (International Physical Activity Questionnaire — Short Form) and original questions about the impact of the COVID-19 pandemic on lifestyle, including physical activity assessed on the scale –3/0/3. 363 employees of the IT sector (63 women, 300 men; average age: 29; average BMI 26.17) met the conditions for inclusion in the study.Results. In total, 26.17% were in the insufficient group, 54% in the sufficient group and 19.83% in the high physical activity group. Overall, 51.24% estimated that the pandemic had a negative effect on their physical activity, 31.40% had no effect, and 17.36% had a positive impact.Conclusions. IT sector employees are mostly characterized by low physical activity. Therefore, they meet the WHO guidelines for the amount of physical activity with a positive effect on health. There are no interactions in mentioned population between undertaking various types and intensities of physical activity and gender, working shifts and working methods. In future research on physical activity, it is worth considering other factors that may be behind it.Wstęp. Ocena aktywności fizycznej pracowników sektora IT podczas pandemii COVID-19 może pomóc dostrzec jej hipotetyczny związek z płcią, formą pracy czy innymi czynnikami. Materiał i metody. Badanie trwało od 29 lipca 2021 do 14 września 2021. W celu dokonania oceny przeprowadzono ankietę opartą o IPAQ-SF (International Physical Activity Questionnaire — Short Form) oraz autorskie pytania dotyczące wpływu pandemii COVID-19 na styl życia, w tym aktywność fizyczną ocenianą w skali –3/0/3.Warunki włączenia do badania spełniło 363 pracowników sektora IT (63 kobiety, 300 mężczyzn, średni wiek: 29 lat, średni wskaźnik masy ciała: 26,17 kg/m2). Wyniki. Łącznie 26,17% badanych znajdowało się w grupie niewystarczającej, 54% w grupie dostatecznej i 19,83% w grupie wysokiej aktywności fizycznej. Oszacowano, że pandemia miała negatywny wpływ na aktywność fizyczną wśród 51,24% badanych, brak wpływu — 31,40%, a pozytywny wpływ u 17,36%. Wnioski. Wśród badanych pracowników sektora IT, większość wykazywała niską aktywność fizyczną. W związku z tym spełniają oni warunki wytycznych Światowej Organizacji Zdrowia dotyczące podejmowanej aktywności fizycznej o pozytywnym wpływie na zdrowie. Wśród tej populacji nie dostrzeżono powiązania między podejmowaniem aktywności fizycznej różnego rodzaju i o różnej intensywności a płcią, formą pracy ani zmianą, podczas której się pracuje. W kolejnych badaniach dotyczących aktywności fizycznej warto rozważyć wzięcie pod uwagę innych czynników, które mogą to determinować

COVID-19 and athletes: Endurance sport and activity resilience study—CAESAR study

Background: The COVID-19 pandemic and imposed restrictions influenced athletic societies, although current knowledge about mild COVID-19 consequences on cardiopulmonary and physiologic parameters remains inconclusive. This study aimed to assess the impact of mild COVID-19 inflection on cardiopulmonary exercise test (CPET) performance among endurance athletes (EA) with varied fitness level.Materials and Methods: 49 EA (nmale = 43, nfemale = 6, mean age = 39.94 ± 7.80 yr, height = 178.45 cm, weight = 76.62 kg; BMI = 24.03 kgm−2) underwent double treadmill or cycle ergometer CPET and body analysis (BA) pre- and post-mild COVID-19 infection. Mild infection was defined as: (1) without hospitalization and (2) without prolonged health complications lasting for >14 days. Speed, power, heart rate (HR), oxygen uptake (VO2), pulmonary ventilation, blood lactate concentration (at the anaerobic threshold (AT)), respiratory compensation point (RCP), and maximum exertion were measured before and after COVID-19 infection. Pearson’s and Spearman’s r correlation coefficients and Student t-test were applied to assess relationship between physiologic or exercise variables and time.Results: The anthropometric measurements did not differ significantly before and after COVID-19. There was a significant reduction in VO2 at the AT and RCP (both p < 0.001). Pre-COVID-19 VO2 was 34.97 ± 6.43 ml kg·min−1, 43.88 ± 7.31 ml kg·min−1 and 47.81 ± 7.81 ml kg·min−1 respectively for AT, RCP and maximal and post-COVID-19 VO2 was 32.35 ± 5.93 ml kg·min−1, 40.49 ± 6.63 ml kg·min−1 and 44.97 ± 7.00 ml kg·min−1 respectively for AT, RCP and maximal. Differences of HR at AT (p < 0.001) and RCP (p < 0.001) was observed. The HR before infection was 145.08 ± 10.82 bpm for AT and 168.78 ± 9.01 bpm for RCP and HR after infection was 141.12 ± 9.99 bpm for AT and 165.14 ± 9.74 bpm for RCP. Time-adjusted measures showed significance for body fat (r = 0.46, p < 0.001), fat mass (r = 0.33, p = 0.020), cycling power at the AT (r = −0.29, p = 0.045), and HR at RCP (r = −0.30, p = 0.036).Conclusion: A mild COVID-19 infection resulted in a decrease in EA’s CPET performance. The most significant changes were observed for VO2 and HR. Medical Professionals and Training Specialists should be aware of the consequences of a mild COVID-19 infection in order to recommend optimal therapeutic methods and properly adjust the intensity of training

Respiratory muscle training induces additional stress and training load in well-trained triathletes—randomized controlled trial

Background: Respiratory muscle training (RMT) has been investigated in the context of improved athletic performance and pulmonary function. However, psychophysiological costs of RMT remain understudied. Voluntary isocapnic hyperpnoea (VIH) and inspiratory pressure threshold loading (IPTL) are widely applied RMT methods. The main purposes of this study were to assess whether RMT induces additional load on well-trained triathletes and determine differences in RMT-induced load between sexes and applied methods.Materials and Methods: 16 well-trained triathletes (n = 16, 56% males) underwent 6 weeks of VIH or IPTL program with progressive overload. Blood markers, subjective measures, cardiac indices, near-infrared spectroscopy indices, inspiratory muscle fatigue, and RMT-induced training load were monitored pre-, in and post-sessions. We used multiple ANOVA to investigate effects of sex, training method, and time on measured parameters.Results: There were significant interactions for acid-base balance (p = 0.04 for sex, p < 0.001 for method), partial carbon dioxide pressure (p = 0.03 for sex, p < 0.001 for method), bicarbonate (p = 0.01 for method), lactate (p < 0.001 for method), RMT-induced training load (p = 0.001 for method for single session, p = 0.03 for method per week), average heart rate (p = 0.03 for sex), maximum heart rate (p = 0.02 for sex), intercostales muscle oxygenation (p = 0.007 for testing week), and intercostales muscle oxygenation recovery (p = 0.003 for testing week and p = 0.007 for method).Conclusion: We found that RMT induced additional load in well-trained triathletes. Elicited changes in monitored variables depend on sex and training method. VIH significantly increased subjective training load measures. IPTL was associated with disbalance in blood gasometry, increase in lactate, and reports of headaches and dizziness. Both methods should be applied with consideration in high-performance settings

External validation of VO2max prediction models based on recreational and elite endurance athletes

In recent years, numerous prognostic models have been developed to predict VO2max. Nevertheless, their accuracy in endurance athletes (EA) stays mostly unvalidated. This study aimed to compare predicted VO2max (pVO2max) with directly measured VO2max by assessing the transferability of the currently available prediction models based on their R$^{2}$, calibration-in-the-large, and calibration slope. 5,260 healthy adult EA underwent a maximal exertion cardiopulmonary exercise test (CPET) (84.76% male; age 34.6±9.5 yrs.; VO2max 52.97±7.39 mL·min$^{-1}$·kg$^{-1}$, BMI 23.59±2.73 kg·m$^{-2}$). 13 models have been selected to establish pVO2max. Participants were classified into four endurance subgroups (high-, recreational-, low- trained, and “transition”) and four age subgroups (18–30, 31–45, 46–60, and ≥61 yrs.). Validation was performed according to TRIPOD guidelines. pVO2max was low-to-moderately associated with direct CPET measurements (p>0.05). Models with the highest accuracy were for males on a cycle ergometer (CE) (Kokkinos R$^{2}$ = 0.64), females on CE (Kokkinos R$^{2}$ = 0.65), males on a treadmill (TE) (Wasserman R$^{2}$ = 0.26), females on TE (Wasserman R$^{2}$ = 0.30). However, selected models underestimated pVO2max for younger and higher trained EA and overestimated for older and lower trained EA. All equations demonstrated merely moderate accuracy and should only be used as a supplemental method for physicians to estimate CRF in EA. It is necessary to derive new models on EA populations to include routinely in clinical practice and sports diagnostic

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. A total of 4043 runners (age = 33.6 (8.1) years; 83.5% males; BMI = 23.7 (2.5) kg·m -2) and 1026 cyclists (age = 36.9 (9.0) years; 89.7% males; BMI = 24.0 (2.7) kg·m -2) underwent maximum CPET. Student t-test, mean absolute percentage error (MAPE), and root mean square error (RMSE) were applied to validate eight running and five cycling HRmax equations externally. HRmax was 184.6 (9.8) beats·min -1 and 182.7 (10.3) beats·min -1, respectively, for running and cycling, p = 0.001. Measured and predicted HRmax differed significantly ( p = 0.001) for 9 of 13 (69.2%) models. HRmax was overestimated by eight (61.5%) and underestimated by five (38.5%) formulae. Overestimated HRmax amounted to 4.9 beats·min -1 and underestimated HRmax was in the range up to 4.9 beats·min -1. RMSE was 9.1-10.5. MAPE ranged to 4.7%. Prediction models allow for limited precision of HRmax estimation and present inaccuracies. HRmax was more often underestimated than overestimated. Predicted HRmax can be implemented for EA as a supplemental method, but CPET is the preferable method

COVID-19 Pandemic Consequences among Individuals with Eating Disorders on a Clinical Sample in Poland—A Cross-Sectional Study

The COVID-19 pandemic and imposed restrictions had negative consequences on overall health among many populations. This study aimed to investigate the influence of the pandemic on eating disorders (ED) and mental health (MH) of individuals with confirmed ED diagnoses. A survey consisting of questions related to (1) diagnosis of COVID-19, (2) changes in ED symptoms and onset of new symptoms, (3) psychological and MH aspects regarding to the pandemic, (4) lifestyle changes, and (5) social media (SM) usage was distributed between April–June 2021. One hundred and ninety-eight individuals met all of the inclusion criteria (nfemales = 195, 98.48%; nother gender = 3, 1.52%). Of the participants, 78.79% reported worsening of their ED symptoms, 42.93% of them noticed an onset of new ED symptoms, and 57.58% believed that the pandemic had a negative impact on their ED treatment. Negative changes due to the pandemic on MH were reported by 88.89%. Of the participants, 91.92% increased their time spent on SM and 54.04% of them declared that it had a negative impact on their MH. Medical professionals should consider results while providing comprehensive psychological care, which can be crucial information in the application of the appropriate treatment strategy

Modeling Physiological Predictors of Running Velocity for Endurance Athletes

Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 53.20 ± 7.17 mL·min−1·kg−1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA

VO2max prediction based on submaximal cardiorespiratory relationships and body composition in male runners and cyclists: a population study

Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work

The Path of a Cardiac Patient—From the First Symptoms to Diagnosis to Treatment: Experiences from the Tertiary Care Center in Poland

Cardiovascular diseases (CVDs) are major concerns in the healthcare system. An individual diagnostic approach and personalized therapy are key areas of an effective therapeutic process. The major aims of this study were: (1) to assess leading patient problems related to symptoms, diagnosis, and treatment of CVDs, (2) to examine patients’ opinions about the healthcare system in Poland, and (3) to provide a proposal of practical solutions. The 27-point author’s questionnaire was distributed in the Cardiology Department of the Tertiary Care Centre between 2nd September–13th November 2021. A total of 132 patients were recruited, and 82 (62.12%; nmale = 37, 45.12%; nfemale = 45, 54.88%) was finally included. The most common CVDs were arrhythmias and hypertension (both n = 43, 52.44%). 23 (28.05%) patients had an online appointment. Of the patients, 66 (80.49%) positively assessed and obtained treatment, while 11 (13.41%) patients declared they received a missed therapy. The participants identified: (1) waiting time (n = 31; 37.80%), (2) diagnostic process (n = 18; 21.95%), and (3) high price with limited availability of drugs (n = 12; 14.63%) as the areas that needed the strongest improvement. Younger patients more often negatively assessed doctor visits (30–40 yr.; p = 0.02) and hospital interventions (40–50 yr.; p = 0.008). Older patients (50–60 years old) less often negatively assessed the therapeutic process (p = 0.01). The knowledge of the factors determining patient adherence to treatment and satisfaction by Medical Professionals is crucial in providing effective treatment. Areas that require the strongest improvement are: (1) waiting time for an appointment and diagnosis, (2) limited availability and price of drugs, and (3) prolonged, complicated diagnostic process. Providing practical solutions is a crucial aspect of improving CVDs therapy

Transferability of Cardiopulmonary Parameters between Treadmill and Cycle Ergometer Testing in Male Triathletes—Prediction Formulae

Cardiopulmonary exercise testing (CPET) on a treadmill (TE) or cycle ergometry (CE) is a common method in sports diagnostics to assess athletes’ aerobic fitness and prescribe training. In a triathlon, the gold standard is performing both CE and TE CPET. The purpose of this research was to create models using CPET results from one modality to predict results for the other modality. A total of 152 male triathletes (age = 38.20 ± 9.53 year; BMI = 23.97 ± 2.10 kg·m−2) underwent CPET on TE and CE, preceded by body composition (BC) analysis. Speed, power, heart rate (HR), oxygen uptake (VO2), respiratory exchange ratio (RER), ventilation (VE), respiratory frequency (fR), blood lactate concentration (LA) (at the anaerobic threshold (AT)), respiratory compensation point (RCP), and maximum exertion were measured. Random forests (RF) were used to find the variables with the highest importance, which were selected for multiple linear regression (MLR) models. Based on R2 and RF variable selection, MLR equations in full, simplified, and the most simplified forms were created for VO2AT, HRAT, VO2RCP, HRRCP, VO2max, and HRmax for CE (R2 = 0.46–0.78) and TE (R2 = 0.59–0.80). By inputting only HR and power/speed into the RF, MLR models for practical HR calculation on TE and CE (both R2 = 0.41–0.75) were created. BC had a significant impact on the majority of CPET parameters. CPET parameters can be accurately predicted between CE and TE testing. Maximal parameters are more predictable than submaximal. Only HR and speed/power from one testing modality could be used to predict HR for another. Created equations, combined with BC analysis, could be used as a method of choice in comprehensive sports diagnostics